I have a small code project dedicated to analyzing floating one-way carshare usage data. It’s called electric2go and is open source.
Floating carshare systems offer cars for rent by the minute or hour. Cars do not have to be returned to where they were picked up, but can be dropped off almost anywhere within a specified operating area and picked up from there by another member for the next trip. This mimics most bikeshare operations, and in a sense provides a taxi you drive yourself.
car2go and Drivenow are the major multi-city operators, with some regional systems like Enjoy in five Italian cities, Communauto Automobile in Montréal, and Evo in Vancouver.
I have previously done a bit of statistics and some availability map animations for selected cities. But there’s a lot more that can be done. Here are some of the possible questions for further study and research.
This post is equal parts roadmap, to-do list, motivation for me, inspiration for others, and a call out for the interested.
There are some obvious statistics: where is the service used most – densest cores, spread-out suburbs, in-between? When are trips most frequently started? What are the differences in use in mornings vs evenings vs late nights; on weekdays vs weekdays? What is the median parking duration, how much of total parked time do parking periods over 12 hours comprise, where are the cars parked the longest, vs the shortest?
How can we quantify availability of carshare vehicles? One idea could be to calculate area (in square kilometers) of a neighbourhood/borough; for any given time, calculate area within 400 m of a carshare vehicle and divide the two to get a ratio; then repeat over time to get graphs of when the given area is has best carshare availability.
From quick analyses, in Canadian cities, floating carshare usage is correlated with population density. But I think there is more. For example, is one-way carsharing more popular in cities with a grid system of streets vs radial patterns? In bigger cities or smaller? Is there an optimal population density for this kind of carsharing? It might not work very well in Hong Kong. Can we model and predict usage of the systems, how long a car will be parked, where it will move next?
Carshare is fundamentally about transportation, so compare it with other transportation methods. Compared to a car parked outside a person’s house, how much trip time does walking to a carshare vehicle add?
Compared to a transit trip, how much time is saved, and on what kind of trips does carshare save the most time? Pick out a sample of trips and run its start and end points through a transit planner specifying time and day of week, to see how driving compared with transit. Which carshare trips – which origin-destination pairs, which times of day – save the most time, and which ones overlap with transit the most?
Are floating carshares used as “last-mile” transportation, travelling to and from rapid transit stations? Could they be – how much time would that save? (E.g.: Calgary/Montréal/Vancouver, outside of downtowns: how many trips between 7 and 10 am end within 200 m of a rapid transit station?) How does the total trip duration and cost compare with using a traditional taxi?
Systems other than car2go often have a choice of vehicles and car2go has started introducing various size 4 and 5-door vehicles as well. Compare the usage of 2-door vs 4-door car2go vehicles; is car2go used more now that they’ve introduced the option? How does usage of 4-door car2go vehicles compare with vehicles from competing systems? In cities with multiple systems (e.g. Milan has four currently!), how does each system’s usage compare and why is it different?
Looking farther into the future, try to estimate how many fewer cars would be needed if vehicles could reposition themselves. Driving back to a busy area at peak hours is an obvious application. However, it would be equally interesting to quantify if and how last-mile connectivity in less dense areas could be improved if a car can move itself slightly (say 1 km) to be closer to the hailer.
Maintenance and operations
Much has been written about plans for transportation services based on self-driving cars. I believe that much of motivation for the car manufacturers running carshare programs is hedging and research for such a possibility. Through their efforts, we can also learn what might be required.
A key of carshare operations is that there is no central base where vehicles are returned and could be maintained. More important than rare repairs or yearly maintenance are everyday tasks required for cars, particularly intensively used cars: cleaning and refueling/charging. Although users are expected to keep the cars relatively clean, over dozens of rentals dirt will inevitably accumulate, especially in rainy or snowy weather. Accidental spills and sickness happen and must also be cleaned up. In a floating carshare, vehicles are also sometimes relocated by the employees.
The maintenance requirements of a self-driving fleet would be fairly similar to that of a floating carshare. It will be useful to see when, where, and how maintenance is currently done.
For an example implementation, pick around 100 cars within a fleet at random. Over one month, chart their cleanliness ratings and fuel level as a scatter plot: x axis = time since last clean/refuel, y axis = fuel level or cleanliness rating. We’ll see the cleanliness spike and the fuel go to 100% when they are cared for. By measuring time between spikes we can determine how much maintenance a car needs.
For finding relocations of vehicles by staff, scatter-plot time spent parked vs hour of day when it was finally moved. I would expect to find a specific time of day where a lot of the long-parked cars are moved, for example 11 am as it is during work hours and should have relatively low road traffic.
There is, of course, a lot more; I also have a technically-oriented list. Contact me if you’re interested in any of the above!